Overview

Dataset statistics

Number of variables14
Number of observations9578
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Categorical3
Numeric11

Alerts

int.rate is highly overall correlated with ficoHigh correlation
fico is highly overall correlated with int.rate and 1 other fieldsHigh correlation
revol.bal is highly overall correlated with revol.utilHigh correlation
revol.util is highly overall correlated with fico and 1 other fieldsHigh correlation
inq.last.6mths is highly overall correlated with credit.policyHigh correlation
credit.policy is highly overall correlated with inq.last.6mthsHigh correlation
revol.bal has 321 (3.4%) zerosZeros
revol.util has 297 (3.1%) zerosZeros
inq.last.6mths has 3637 (38.0%) zerosZeros
delinq.2yrs has 8458 (88.3%) zerosZeros
pub.rec has 9019 (94.2%) zerosZeros

Reproduction

Analysis started2024-10-04 17:43:38.823442
Analysis finished2024-10-04 17:45:17.205741
Duration1 minute and 38.38 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

credit.policy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.7 KiB
1
7710 
0
1868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

Length

2024-10-04T18:45:17.674370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T18:45:18.236867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

Most occurring characters

ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9578
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7710
80.5%
0 1868
 
19.5%

purpose
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size149.7 KiB
debt_consolidation
3957 
all_other
2331 
credit_card
1262 
home_improvement
629 
small_business
619 
Other values (2)
780 

Length

Max length18
Median length16
Mean length14.064314
Min length9

Characters and Unicode

Total characters134708
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowcredit_card
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowcredit_card

Common Values

ValueCountFrequency (%)
debt_consolidation 3957
41.3%
all_other 2331
24.3%
credit_card 1262
 
13.2%
home_improvement 629
 
6.6%
small_business 619
 
6.5%
major_purchase 437
 
4.6%
educational 343
 
3.6%

Length

2024-10-04T18:45:18.596212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T18:45:19.080555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
debt_consolidation 3957
41.3%
all_other 2331
24.3%
credit_card 1262
 
13.2%
home_improvement 629
 
6.6%
small_business 619
 
6.5%
major_purchase 437
 
4.6%
educational 343
 
3.6%

Most occurring characters

ValueCountFrequency (%)
o 16240
12.1%
t 12479
9.3%
e 10836
 
8.0%
d 10781
 
8.0%
i 10767
 
8.0%
l 10200
 
7.6%
a 9729
 
7.2%
n 9505
 
7.1%
_ 9235
 
6.9%
c 7261
 
5.4%
Other values (9) 27675
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125473
93.1%
Connector Punctuation 9235
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16240
12.9%
t 12479
9.9%
e 10836
8.6%
d 10781
8.6%
i 10767
8.6%
l 10200
8.1%
a 9729
7.8%
n 9505
7.6%
c 7261
 
5.8%
s 6870
 
5.5%
Other values (8) 20805
16.6%
Connector Punctuation
ValueCountFrequency (%)
_ 9235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125473
93.1%
Common 9235
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16240
12.9%
t 12479
9.9%
e 10836
8.6%
d 10781
8.6%
i 10767
8.6%
l 10200
8.1%
a 9729
7.8%
n 9505
7.6%
c 7261
 
5.8%
s 6870
 
5.5%
Other values (8) 20805
16.6%
Common
ValueCountFrequency (%)
_ 9235
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16240
12.1%
t 12479
9.3%
e 10836
 
8.0%
d 10781
 
8.0%
i 10767
 
8.0%
l 10200
 
7.6%
a 9729
 
7.2%
n 9505
 
7.1%
_ 9235
 
6.9%
c 7261
 
5.4%
Other values (9) 27675
20.5%

int.rate
Real number (ℝ)

Distinct249
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12264006
Minimum0.06
Maximum0.2164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:19.689886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.0774
Q10.1039
median0.1221
Q30.1407
95-th percentile0.167
Maximum0.2164
Range0.1564
Interquartile range (IQR)0.0368

Descriptive statistics

Standard deviation0.026846987
Coefficient of variation (CV)0.21890879
Kurtosis-0.22432351
Mean0.12264006
Median Absolute Deviation (MAD)0.0186
Skewness0.16441991
Sum1174.6465
Variance0.00072076072
MonotonicityNot monotonic
2024-10-04T18:45:20.283570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1253 354
 
3.7%
0.0894 299
 
3.1%
0.1183 243
 
2.5%
0.1218 215
 
2.2%
0.0963 210
 
2.2%
0.1114 206
 
2.2%
0.08 198
 
2.1%
0.1287 197
 
2.1%
0.1148 193
 
2.0%
0.0859 187
 
2.0%
Other values (239) 7276
76.0%
ValueCountFrequency (%)
0.06 8
 
0.1%
0.0639 4
 
< 0.1%
0.0676 9
 
0.1%
0.0705 23
 
0.2%
0.0712 9
 
0.1%
0.0714 28
 
0.3%
0.0737 32
0.3%
0.074 72
0.8%
0.0743 33
0.3%
0.0751 38
0.4%
ValueCountFrequency (%)
0.2164 2
 
< 0.1%
0.2121 7
0.1%
0.209 2
 
< 0.1%
0.2086 6
0.1%
0.2052 4
< 0.1%
0.2017 6
0.1%
0.2016 1
 
< 0.1%
0.2011 9
0.1%
0.1982 8
0.1%
0.1979 6
0.1%

installment
Real number (ℝ)

Distinct4788
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.08941
Minimum15.67
Maximum940.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:20.908553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile65.5595
Q1163.77
median268.95
Q3432.7625
95-th percentile756.2655
Maximum940.14
Range924.47
Interquartile range (IQR)268.9925

Descriptive statistics

Standard deviation207.0713
Coefficient of variation (CV)0.64894444
Kurtosis0.13790774
Mean319.08941
Median Absolute Deviation (MAD)124.7
Skewness0.91252246
Sum3056238.4
Variance42878.524
MonotonicityNot monotonic
2024-10-04T18:45:21.861575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317.72 41
 
0.4%
316.11 34
 
0.4%
319.47 29
 
0.3%
381.26 27
 
0.3%
662.68 27
 
0.3%
156.1 24
 
0.3%
320.95 24
 
0.3%
334.67 23
 
0.2%
669.33 23
 
0.2%
188.02 23
 
0.2%
Other values (4778) 9303
97.1%
ValueCountFrequency (%)
15.67 1
< 0.1%
15.69 1
< 0.1%
15.75 1
< 0.1%
15.76 1
< 0.1%
15.91 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
16.73 1
< 0.1%
ValueCountFrequency (%)
940.14 1
 
< 0.1%
926.83 2
< 0.1%
922.42 1
 
< 0.1%
918.02 2
< 0.1%
916.95 2
< 0.1%
914.42 2
< 0.1%
913.63 3
< 0.1%
910.44 1
 
< 0.1%
909.25 1
 
< 0.1%
907.6 2
< 0.1%

log.annual.inc
Real number (ℝ)

Distinct1987
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.932117
Minimum7.5475017
Maximum14.528354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:22.439671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7.5475017
5-th percentile9.9178933
Q110.558414
median10.928884
Q311.291293
95-th percentile11.918391
Maximum14.528354
Range6.9808528
Interquartile range (IQR)0.7328794

Descriptive statistics

Standard deviation0.61481275
Coefficient of variation (CV)0.056239129
Kurtosis1.6090041
Mean10.932117
Median Absolute Deviation (MAD)0.36694576
Skewness0.028668107
Sum104707.82
Variance0.37799472
MonotonicityNot monotonic
2024-10-04T18:45:23.002155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.00209984 308
 
3.2%
10.81977828 248
 
2.6%
10.30895266 224
 
2.3%
10.59663473 224
 
2.3%
10.71441777 221
 
2.3%
11.22524339 196
 
2.0%
11.15625052 165
 
1.7%
10.77895629 149
 
1.6%
10.91508846 147
 
1.5%
11.08214255 146
 
1.5%
Other values (1977) 7550
78.8%
ValueCountFrequency (%)
7.547501683 1
 
< 0.1%
7.60090246 1
 
< 0.1%
8.101677747 1
 
< 0.1%
8.160518247 1
 
< 0.1%
8.188689124 1
 
< 0.1%
8.29404964 3
< 0.1%
8.342839804 1
 
< 0.1%
8.411832676 1
 
< 0.1%
8.476371197 2
< 0.1%
8.494538501 1
 
< 0.1%
ValueCountFrequency (%)
14.52835448 1
 
< 0.1%
14.18015367 1
 
< 0.1%
14.12446477 1
 
< 0.1%
13.99783211 1
 
< 0.1%
13.71015004 2
< 0.1%
13.5670492 2
< 0.1%
13.54370183 1
 
< 0.1%
13.48700649 1
 
< 0.1%
13.47019937 1
 
< 0.1%
13.45883561 3
< 0.1%

dti
Real number (ℝ)

Distinct2529
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.606679
Minimum0
Maximum29.96
Zeros89
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:23.611460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.27
Q17.2125
median12.665
Q317.95
95-th percentile23.65
Maximum29.96
Range29.96
Interquartile range (IQR)10.7375

Descriptive statistics

Standard deviation6.8839695
Coefficient of variation (CV)0.54605734
Kurtosis-0.90035536
Mean12.606679
Median Absolute Deviation (MAD)5.385
Skewness0.023941023
Sum120746.77
Variance47.389037
MonotonicityNot monotonic
2024-10-04T18:45:24.205195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89
 
0.9%
10 19
 
0.2%
0.6 16
 
0.2%
15.1 13
 
0.1%
12 13
 
0.1%
13.16 13
 
0.1%
6 13
 
0.1%
19.2 13
 
0.1%
10.8 12
 
0.1%
15.6 12
 
0.1%
Other values (2519) 9365
97.8%
ValueCountFrequency (%)
0 89
0.9%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 1
 
< 0.1%
0.07 3
 
< 0.1%
0.08 2
 
< 0.1%
0.09 2
 
< 0.1%
0.12 2
 
< 0.1%
ValueCountFrequency (%)
29.96 1
< 0.1%
29.95 1
< 0.1%
29.9 1
< 0.1%
29.74 1
< 0.1%
29.72 1
< 0.1%
29.7 2
< 0.1%
29.6 1
< 0.1%
29.47 1
< 0.1%
29.42 1
< 0.1%
29.41 1
< 0.1%

fico
Real number (ℝ)

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.84631
Minimum612
Maximum827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:24.798871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile657
Q1682
median707
Q3737
95-th percentile782
Maximum827
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation37.970537
Coefficient of variation (CV)0.053415958
Kurtosis-0.42231231
Mean710.84631
Median Absolute Deviation (MAD)25
Skewness0.47125974
Sum6808486
Variance1441.7617
MonotonicityNot monotonic
2024-10-04T18:45:25.330119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
687 548
 
5.7%
682 536
 
5.6%
692 498
 
5.2%
697 476
 
5.0%
702 472
 
4.9%
707 444
 
4.6%
667 438
 
4.6%
677 427
 
4.5%
717 424
 
4.4%
662 414
 
4.3%
Other values (34) 4901
51.2%
ValueCountFrequency (%)
612 2
 
< 0.1%
617 1
 
< 0.1%
622 1
 
< 0.1%
627 2
 
< 0.1%
632 6
 
0.1%
637 5
 
0.1%
642 102
1.1%
647 112
1.2%
652 131
1.4%
657 127
1.3%
ValueCountFrequency (%)
827 1
 
< 0.1%
822 5
 
0.1%
817 6
 
0.1%
812 33
 
0.3%
807 45
 
0.5%
802 55
0.6%
797 76
0.8%
792 97
1.0%
787 85
0.9%
782 118
1.2%

days.with.cr.line
Real number (ℝ)

Distinct2687
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4560.7672
Minimum178.95833
Maximum17639.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:25.908202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum178.95833
5-th percentile1320.0417
Q12820
median4139.9583
Q35730
95-th percentile9329.9583
Maximum17639.958
Range17461
Interquartile range (IQR)2910

Descriptive statistics

Standard deviation2496.9304
Coefficient of variation (CV)0.54748034
Kurtosis1.9378606
Mean4560.7672
Median Absolute Deviation (MAD)1440.0833
Skewness1.1557482
Sum43683028
Variance6234661.3
MonotonicityNot monotonic
2024-10-04T18:45:26.501885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3660 50
 
0.5%
3630 48
 
0.5%
3990 46
 
0.5%
4410 44
 
0.5%
3600 41
 
0.4%
2550 38
 
0.4%
4080 38
 
0.4%
1800 37
 
0.4%
3690 37
 
0.4%
4020 35
 
0.4%
Other values (2677) 9164
95.7%
ValueCountFrequency (%)
178.9583333 1
 
< 0.1%
180.0416667 3
< 0.1%
181 1
 
< 0.1%
183.0416667 1
 
< 0.1%
209.0416667 1
 
< 0.1%
210 1
 
< 0.1%
212.0416667 1
 
< 0.1%
238.9583333 5
0.1%
240.0416667 1
 
< 0.1%
291.9583333 1
 
< 0.1%
ValueCountFrequency (%)
17639.95833 1
< 0.1%
17616 1
< 0.1%
16652 1
< 0.1%
16350 1
< 0.1%
16260 1
< 0.1%
16259.04167 1
< 0.1%
16213 1
< 0.1%
15990 1
< 0.1%
15692 1
< 0.1%
15420.95833 1
< 0.1%

revol.bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7869
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16913.964
Minimum0
Maximum1207359
Zeros321
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:27.111215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile127.7
Q13187
median8596
Q318249.5
95-th percentile57654.3
Maximum1207359
Range1207359
Interquartile range (IQR)15062.5

Descriptive statistics

Standard deviation33756.19
Coefficient of variation (CV)1.9957586
Kurtosis259.6552
Mean16913.964
Median Absolute Deviation (MAD)6488
Skewness11.161058
Sum1.6200195 × 108
Variance1.1394803 × 109
MonotonicityNot monotonic
2024-10-04T18:45:27.751823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 321
 
3.4%
298 10
 
0.1%
255 10
 
0.1%
682 9
 
0.1%
346 8
 
0.1%
2229 6
 
0.1%
182 6
 
0.1%
1085 6
 
0.1%
8035 5
 
0.1%
1 5
 
0.1%
Other values (7859) 9192
96.0%
ValueCountFrequency (%)
0 321
3.4%
1 5
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
6 5
 
0.1%
7 1
 
< 0.1%
9 4
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
1207359 1
< 0.1%
952013 1
< 0.1%
602519 1
< 0.1%
508961 1
< 0.1%
407794 1
< 0.1%
401941 1
< 0.1%
394107 1
< 0.1%
388892 1
< 0.1%
385489 1
< 0.1%
374487 1
< 0.1%

revol.util
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1035
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.799236
Minimum0
Maximum119
Zeros297
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:28.361155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q122.6
median46.3
Q370.9
95-th percentile94
Maximum119
Range119
Interquartile range (IQR)48.3

Descriptive statistics

Standard deviation29.014417
Coefficient of variation (CV)0.6199763
Kurtosis-1.116467
Mean46.799236
Median Absolute Deviation (MAD)24.2
Skewness0.059985443
Sum448243.08
Variance841.83639
MonotonicityNot monotonic
2024-10-04T18:45:28.954863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 297
 
3.1%
0.5 26
 
0.3%
47.8 22
 
0.2%
0.3 22
 
0.2%
73.7 22
 
0.2%
3.3 21
 
0.2%
0.1 21
 
0.2%
0.7 20
 
0.2%
1 20
 
0.2%
0.2 20
 
0.2%
Other values (1025) 9087
94.9%
ValueCountFrequency (%)
0 297
3.1%
0.04 1
 
< 0.1%
0.1 21
 
0.2%
0.2 20
 
0.2%
0.3 22
 
0.2%
0.4 12
 
0.1%
0.5 26
 
0.3%
0.6 12
 
0.1%
0.7 20
 
0.2%
0.8 14
 
0.1%
ValueCountFrequency (%)
119 1
< 0.1%
108.8 1
< 0.1%
106.5 1
< 0.1%
106.4 1
< 0.1%
106.2 1
< 0.1%
106.1 1
< 0.1%
105.7 1
< 0.1%
105.3 1
< 0.1%
105.2 1
< 0.1%
104.3 1
< 0.1%

inq.last.6mths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5774692
Minimum0
Maximum33
Zeros3637
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:29.517292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2002453
Coefficient of variation (CV)1.3947945
Kurtosis26.288131
Mean1.5774692
Median Absolute Deviation (MAD)1
Skewness3.5841509
Sum15109
Variance4.8410794
MonotonicityNot monotonic
2024-10-04T18:45:29.965002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 3637
38.0%
1 2462
25.7%
2 1384
 
14.4%
3 864
 
9.0%
4 475
 
5.0%
5 278
 
2.9%
6 165
 
1.7%
7 100
 
1.0%
8 72
 
0.8%
9 47
 
0.5%
Other values (18) 94
 
1.0%
ValueCountFrequency (%)
0 3637
38.0%
1 2462
25.7%
2 1384
 
14.4%
3 864
 
9.0%
4 475
 
5.0%
5 278
 
2.9%
6 165
 
1.7%
7 100
 
1.0%
8 72
 
0.8%
9 47
 
0.5%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
25 1
 
< 0.1%
24 2
< 0.1%
20 1
 
< 0.1%
19 2
< 0.1%
18 4
< 0.1%

delinq.2yrs
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1637085
Minimum0
Maximum13
Zeros8458
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:30.418074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54621492
Coefficient of variation (CV)3.3365093
Kurtosis71.433182
Mean0.1637085
Median Absolute Deviation (MAD)0
Skewness6.0617933
Sum1568
Variance0.29835074
MonotonicityNot monotonic
2024-10-04T18:45:30.886782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 8458
88.3%
1 832
 
8.7%
2 192
 
2.0%
3 65
 
0.7%
4 19
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
13 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 8458
88.3%
1 832
 
8.7%
2 192
 
2.0%
3 65
 
0.7%
4 19
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 6
 
0.1%
4 19
 
0.2%
3 65
 
0.7%
2 192
 
2.0%
1 832
8.7%

pub.rec
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062121529
Minimum0
Maximum5
Zeros9019
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size149.7 KiB
2024-10-04T18:45:31.433616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26212633
Coefficient of variation (CV)4.219573
Kurtosis38.781007
Mean0.062121529
Median Absolute Deviation (MAD)0
Skewness5.1264345
Sum595
Variance0.068710211
MonotonicityNot monotonic
2024-10-04T18:45:32.152354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9019
94.2%
1 533
 
5.6%
2 19
 
0.2%
3 5
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 9019
94.2%
1 533
 
5.6%
2 19
 
0.2%
3 5
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.1%
2 19
 
0.2%
1 533
 
5.6%
0 9019
94.2%

not.fully.paid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.7 KiB
0
8045 
1
1533 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Length

2024-10-04T18:45:32.558573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T18:45:32.964795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9578
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8045
84.0%
1 1533
 
16.0%

Interactions

2024-10-04T18:45:09.659345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:43:59.984775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:08.499779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:17.121589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:25.870977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:31.943208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:37.770918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:44.129858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:50.332530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:57.832011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:03.862850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:10.174906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:02.140839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:09.265353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:18.012157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:26.761574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:32.240062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:38.552119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:44.520461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:51.207507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:58.425716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:04.394084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:10.612377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:02.656438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:10.030912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:19.121459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:27.558358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:32.599401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:39.239568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:44.989160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:52.410518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:58.941304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:04.862779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:11.081081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:03.218893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:10.858980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:19.871432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:28.355208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:32.818131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:39.927042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:45.442274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:53.144847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:59.410033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:05.347145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:11.549834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:03.765726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:11.824998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:20.668219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:28.948887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:33.083742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:40.661337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:45.942234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:53.972938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:00.019356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:05.847110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:12.002900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:04.250101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:12.981166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:21.402539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:29.526996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:33.271223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:41.145675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:46.239085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:54.504117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:00.597439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:06.300200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:12.487269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:04.859411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:13.774149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:22.308728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:30.052699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:33.771197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:41.801903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:46.551564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:55.050960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:01.331799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:06.893874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:12.987235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:05.406242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:14.215549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:23.152431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:30.458943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:34.443027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:42.364362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:47.332754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:55.550953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:01.894222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:07.706315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:13.455926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:06.156213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:14.902993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:23.855524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:30.896410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:35.599200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:42.848686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:48.051453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:56.082139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:02.394188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:08.206281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:13.940295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:07.124865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:15.684189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:24.386741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:31.333881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:36.411629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:43.379912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:48.817027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:56.738337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:02.878567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:08.690623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:14.424611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:07.874851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:16.402924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:25.230431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:31.677603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:37.145965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:43.770506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:49.535719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:44:57.269545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:03.378533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-10-04T18:45:09.175002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2024-10-04T18:45:33.339768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
int.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.reccredit.policypurposenot.fully.paid
int.rate1.0000.2430.0420.216-0.745-0.1340.1490.4730.1740.1730.0940.3090.1110.159
installment0.2431.0000.4310.0630.0850.2020.3520.096-0.007-0.008-0.0280.0950.1400.068
log.annual.inc0.0420.4311.000-0.0600.1080.4000.4160.0530.0310.0300.0130.1150.1020.059
dti0.2160.063-0.0601.000-0.2140.0730.3760.3340.028-0.0180.0090.2050.1010.031
fico-0.7450.0850.108-0.2141.0000.252-0.095-0.520-0.174-0.237-0.1480.4640.0820.153
days.with.cr.line-0.1340.2020.4000.0730.2521.0000.324-0.004-0.0420.0950.1020.1790.0660.029
revol.bal0.1490.3520.4160.376-0.0950.3241.0000.515-0.023-0.054-0.0260.2170.0380.052
revol.util0.4730.0960.0530.334-0.520-0.0040.5151.000-0.016-0.0320.0710.1070.1220.082
inq.last.6mths0.174-0.0070.0310.028-0.174-0.042-0.023-0.0161.0000.0210.0560.6520.0320.146
delinq.2yrs0.173-0.0080.030-0.018-0.2370.095-0.054-0.0320.0211.0000.0010.0710.0000.000
pub.rec0.094-0.0280.0130.009-0.1480.102-0.0260.0710.0560.0011.0000.0560.0290.060
credit.policy0.3090.0950.1150.2050.4640.1790.2170.1070.6520.0710.0561.0000.0410.157
purpose0.1110.1400.1020.1010.0820.0660.0380.1220.0320.0000.0290.0411.0000.097
not.fully.paid0.1590.0680.0590.0310.1530.0290.0520.0820.1460.0000.0600.1570.0971.000

Missing values

2024-10-04T18:45:15.268321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-04T18:45:16.190107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

credit.policypurposeint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paid
01debt_consolidation0.1189829.1011.35040719.487375639.9583332885452.10000
11credit_card0.1071228.2211.08214314.297072760.0000003362376.70000
21debt_consolidation0.1357366.8610.37349111.636824710.000000351125.61000
31debt_consolidation0.1008162.3411.3504078.107122699.9583333366773.21000
41credit_card0.1426102.9211.29973214.976674066.000000474039.50100
51credit_card0.0788125.1311.90496816.987276120.0416675080751.00000
61debt_consolidation0.1496194.0210.7144184.006673180.041667383976.80011
71all_other0.1114131.2211.00210011.087225116.0000002422068.60001
81home_improvement0.113487.1911.40756517.256823989.0000006990951.11000
91debt_consolidation0.122184.1210.20359210.007072730.041667563023.01000
credit.policypurposeint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paid
95680all_other0.197937.0610.64542522.176675916.0000002885459.86010
95690home_improvement0.1426823.3412.4292163.627223239.9583333357583.95001
95700all_other0.1671113.6310.64542528.066723210.0416672575963.85001
95710all_other0.1568161.0111.2252438.006777230.000000690929.24011
95720debt_consolidation0.156569.9810.1104727.026628190.041667299939.56001
95730all_other0.1461344.7612.18075510.3967210474.00000021537282.12001
95740all_other0.1253257.7011.1418620.217224380.0000001841.15001
95750debt_consolidation0.107197.8110.59663513.096873450.0416671003682.98001
95760home_improvement0.1600351.5810.81977819.186921800.00000003.25001
95770debt_consolidation0.1392853.4311.26446416.287324740.0000003787957.06001